BioSystems, 17 (1984) 75--76 75 Elsevier Scientific Publishers Ireland Ltd.

EDITORIAL ANNOUNCEMENT -- A CALL FOR PAPERS

Staging of Computer Models in the Life Sciences

Papers on computat ional modeling of complex biological systems are increasingly visible in the pages of BioSystems. During the past year the editors have decided to encourage this area concertedly. At the same time Dr. R.J. Ransom made the suggestion that a journal of computer models in biology would be timely.

But how should such a journal present itself?. A journal specific to computer models might lose contact with biological concepts and phenomenology. It p robably would lose contact with important segments of the biological community . Computer models have both a theoretical and experimental aspect. Creation of models is a theoretical enterprise. But once created they are most effectively studied using experimental methods. Furthermore models using similar method° ologies can deal with themes as diverse as evolution, development, the brain, or immunity. A journal with broad coverage and sympathy towards theoretical as well as experimental approaches is necessary. Fortunately such a journal is at hand. It is BioSystems.

Computer modeling of biological nature can perhaps be usefully conceived of as a series of stagings. The precondition for such staging is adequate computing machinery along with the language tools for algorithmically mapping the subtle complexities of biological realities. The first stage involves framework establishment and model formulation. The second, implementa- tion. The third, that of model tuning, is the stage of prediscovery. This is an important stage since it usually requires considerable reconceptualization. Many ideas which may have been broadly accepted by the biological communi ty fail the test of effective implementation. The fourth stage is that of observation of the program in action. This is the stage of discovery through comparison and contrast with expectations. The fifth stage is that of controlled experimentation directed to verifying hypotheses about the operations of the model. The insights obtained are significant to the extent that the model usefully reflects nature. The sixth stage is that of inter- preting the significance of the results in the light of the simplifications and distortions which inevitably accompany any a t tempt to formalize nature. A seventh stage (not necessarily seventh in time) may involve new analytic models whose assumptions are justified by the computat ional study. Often working with a computer model will suggest the utility of novel analytic theories of a traditional mathematical nature. The eighth stage involves adequate documentat ion and communication.

There are many levels of documentat ion. Anyone familiar with large scale computer modeling knows that continuous documentat ion is necessary. Members of the team working on the model must communicate with each other. Each individual must communicate with himself at a later point in time. The creators of the model must communicate with other investigators who wish to understand its structure in fine detail. They must communicate with investigators who may wish to use the model or perhaps to couple it to their own experimental work. For this purpose the model must be put into a portable form, acceptable to as many computing machines as possible. The creators of the model should convert it into a modeling system (a ninth stage) whose assumptions can be easily modified, to suit different users. In its first eight stages the model is a tool of discovery for the team which created it. In the ninth stage it is transformed into a class of models which becomes a tool of discovery for the community .

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It is also necessary to communicate the model to a broader scientific community who wish to have an artful description of its contents, an accurate impression of the simplifications made, an d a scholarly evaluation of its performance.

On the average it takes about a year's work to develop a tuned implementation of a significant non-standard computer model. By non-standard I mean a model which does not use pre-existing programs to depict nature. In a sense such "canned" algorithms are already user oriented models. They are not to be dismissed. But they are useful only for those natural phenomena whose structure is isomorphic to previously existing natural phenomena. When using the computer as a true tool for exploring the unknown one should be careful not to exclude phenomena which are disanalogous to those already known. The whole value of the powerful language tools of com- puter science is that they enable us to escape such constraints. Canned programs which trivialize large classes of problems are thus not likely to be the best tools of discovery, though they may be excellent tools for design subsequent to the discovery stages.

On the average a thorough series of experiments with a developed model requires another year of work. Models once developed can be continuously upgraded. The lifetime of any kind of software is fairly short. The tenth stage is thus that of maintenance. The experience in industry is that the maintenance stage is often the most expensive. The experience in scientific modeling will probably be even more complicated. Models used as research tools must be continuously transformed. The problem of documentat ion and communication is thus compounded. Of course some models will probably be taken over by industry and harnessed for production purposes {for example, in the biotechnology industry).

It is evident that the problem of model communication is a momentous one. As more and more groups begin to stage computer models in the biological sciences this problem of communication will clearly become more and more important. The requirements for communication and docu- mentation at different stages will be quite different. The requirement for the publication of base line descriptions and follow up experiments will vary with different stages of model maturity. The standards in this area are evolving; different groups of workers will undoubtedly create alternative styles of explication. It would be counterproductive at this point to predefine these styles. The editors intend that BioSystems will serve as a vehicle, perhaps as the main vehicle, for the advancement of the state of the art in the use of the computer as a tool of discovery in the biological sciences.

Michael Conrad